Abstract
Siamese visual trackers have recently advanced through increasingly sophisticated fusion mechanisms built on convolutional or Transformer architectures. However, both struggle to deliver pixel-level interactions efficiently on resource-constrained hardware, leading to a persistent accuracy-efficiency imbalance. Motivated by this limitation, we redesign the Siamese neck with a simple yet effective Multilayer Perception (MLP)-based fusion module that enables pixel-level interaction with minimal structural overhead. Nevertheless, naively stacking MLP blocks introduces a new challenge: computational cost can scale quadratically with channel width. To overcome this, we construct a hierarchical search space of carefully designed MLP modules and introduce a customized relaxation strategy that enables differentiable neural architecture search (DNAS) to decouple channel-width optimization from other architectural choices. This targeted decoupling automatically balances channel width and depth, yielding a low-complexity architecture. The resulting tracker achieves state-of-the-art accuracy-efficiency trade-offs. It ranks among the top performers on four general-purpose and three aerial tracking benchmarks, while maintaining real-time performance on both resource-constrained Graphics Processing Units (GPUs) and Neural Processing Units (NPUs).
Abstract (translated)
最近，暹罗视觉跟踪器通过基于卷积或Transformer架构的日益复杂的融合机制得到了改进。然而，这两种方法都在资源受限的硬件上难以高效地实现像素级别的交互，导致了持久存在的精度与效率之间的不平衡问题。鉴于这一局限性，我们重新设计了暹罗颈部，采用了一种简单而有效的多层感知器（MLP）融合模块，该模块能够以最少的结构开销实现像素级互动。然而，直接堆叠MLP块会带来一个新的挑战：计算成本可能会随着通道宽度呈二次方增长。为了解决这个问题，我们构建了一个精心设计的MLP模块层次搜索空间，并引入了一种定制化的松弛策略，使可微神经架构搜索（DNAS）能够将通道宽度优化与其他架构选择解耦。这种有目标的解耦可以自动平衡通道宽度与深度，从而产生低复杂度的架构。由此产生的跟踪器在精度和效率之间的权衡上达到了最先进的水平，在四个通用目的和三个空中追踪基准测试中表现出顶级性能，并且能够在资源受限的图形处理器（GPU）和神经处理单元（NPU）上保持实时性能。
URL
https://arxiv.org/abs/2603.01706
